14,348 research outputs found
A General Large Neighborhood Search Framework for Solving Integer Programs
This paper studies how to design abstractions of large-scale combinatorial optimization problems that can leverage existing state-of-the-art solvers in general purpose ways, and that are amenable to data-driven design. The goal is to arrive at new approaches that can reliably outperform existing solvers in wall-clock time. We focus on solving integer programs, and ground our approach in the large neighborhood search (LNS) paradigm, which iteratively chooses a subset of variables to optimize while leaving the remainder fixed. The appeal of LNS is that it can easily use any existing solver as a subroutine, and thus can inherit the benefits of carefully engineered heuristic approaches and their software implementations. We also show that one can learn a good neighborhood selector from training data. Through an extensive empirical validation, we demonstrate that our LNS framework can significantly outperform, in wall-clock time, compared to state-of-the-art commercial solvers such as Gurobi
Social Data Offloading in D2D-Enhanced Cellular Networks by Network Formation Games
Recently, cellular networks are severely overloaded by social-based services,
such as YouTube, Facebook and Twitter, in which thousands of clients subscribe
a common content provider (e.g., a popular singer) and download his/her content
updates all the time. Offloading such traffic through complementary networks,
such as a delay tolerant network formed by device-to-device (D2D)
communications between mobile subscribers, is a promising solution to reduce
the cellular burdens. In the existing solutions, mobile users are assumed to be
volunteers who selfishlessly deliver the content to every other user in
proximity while moving. However, practical users are selfish and they will
evaluate their individual payoffs in the D2D sharing process, which may highly
influence the network performance compared to the case of selfishless users. In
this paper, we take user selfishness into consideration and propose a network
formation game to capture the dynamic characteristics of selfish behaviors. In
the proposed game, we provide the utility function of each user and specify the
conditions under which the subscribers are guaranteed to converge to a stable
network. Then, we propose a practical network formation algorithm in which the
users can decide their D2D sharing strategies based on their historical
records. Simulation results show that user selfishness can highly degrade the
efficiency of data offloading, compared with ideal volunteer users. Also, the
decrease caused by user selfishness can be highly affected by the cost ratio
between the cellular transmission and D2D transmission, the access delays, and
mobility patterns
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